1. Field of the Invention
This invention pertains generally to wireless systems and, more particularly, to systems for synchronizing wireless nodes of a wireless communication network. The invention also pertains to methods for synchronizing wireless nodes of a wireless communication network.
2. Background Information
Wireless sensor networks (WSN) enable cabling reduction, reduced installation times and operation in hazardous settings for many industrial solutions.
In a WSN, dependability metrics (e.g., reliability; availability; safety; security; survivability; maintainability) are related to the architectural choice (e.g., processing resources; wireless technology) and the particular propagation media characteristics of the industrial setting under study, which are known to be assessed via site surveys. In site surveys, the radio frequency (RF) propagation and interference characteristics are measured at the site of a proposed or actual wireless network installation. With this information, the network engineer can identify under-performing wireless links and make the necessary decisions (e.g., MAC/PHY layer selection; node distribution) to meet the required dependability levels. The site survey and, thus, the surveying instrument, must consider not only dependability metrics, but also industrial conditions, in terms of cost, management, resource sharing, experimental control, data analysis, applicability and repeatability, which differ from other residential, academic or commercial environments.
At the plant floor, which typically includes a plethora of metallic surfaces, non-isotropic signal path losses among radios will be created due to reflection, diffraction and scattering. The measured reception rate between two nodes will not necessarily correlate with the distance between the nodes as is widely observed in other wireless measurements. When factors like node hardware platform and EMI generation by existing operational equipment are taken into account, it is simply not possible to reproduce field conditions in the lab.
Correct operation of low-power wireless systems is dependent on the environment in which they operate. Environmental effects that impact correct operation include, for example, path loss, multi-path fading, shadowing, interference and jamming. These environmental effects vary over time, such that a low-power wireless system that functions correctly at the present time may not function correctly in the future.
Assessment of the RF environment is a crucial factor before any wireless network deployment is attempted. It is believed that known methods and instruments are either designed for generic purposes or are optimized for traditional cellular-radio networks. The unique characteristics of low-power and low-cost distributed wireless networks demand methods and instruments that are flexible, scalable, and able to estimate more accurately the actual performance of the WSN before deployment.
Whether or not a packet of information is successfully transmitted from one wireless node to another wireless node is determined by many characteristics of the wireless equipment (e.g., without limitation, antenna design; transmit power; modulation) and also by characteristics of the environment. A site survey for wireless network deployment measures the characteristics of the environment, so that the characteristics of the equipment can be suitably engineered. Typical design goals are low cost, high data capacity and high reliability. Typical design parameters are radio location, network parameters (of which there are many, such as network topology and the logic of retransmit requests) and transmit power. The most important environmental characteristics are the propagation channel, which, when all other things are fixed, determines the strength of signals arriving at the receiver, and interference, which arises with other RF emitters in the environment. The environmental characteristics are substantially site specific. A door being open or closed, or the style of construction (e.g., aluminum studs), or indeed a person standing in a certain location can significantly change the propagation channel characteristics. Similarly, a piece of equipment turned on or uncovered can change the interfering signal strength. In order to choose the design parameters to meet the design goals, it is necessary to know the values of site-specific environmental characteristics. These can either be assigned generic values, with the risk of excess cost or non-performance, or can be measured in a site survey.
There are two known site survey methods. First, there is the use of RF test instruments (such as sources and a spectrum analyzer) to measure the RF environment, such as propagation channel characteristics and interference signals. Second, a wireless communication network is installed and its performance is measured with a communication network that uses a wired backbone.
Many known tools for wireless performance measurement depend on wiring for correct operation. Such wiring typically provides power, and control signals and data logging from/to a central point. However, it is often cumbersome and unsafe to route wires in an existing commercial or industrial environment. For example, an artificial ground plane, such as is created by the control, data or power wiring of known prior systems, can distort the measurements and lead to a false assessment of communication performance.
The known seven-layer ISO/OSI communication model (i.e., Application, Presentation, Session, Transport, Network, Data Link and Physical layers) is a way of representing the several components of a communication system. Each of the seven layers represents more sophisticated actions or services, based on the layers below. Using RF test instruments is a layer 1 (Physical layer) test. Operating a network and measuring its performance is a layer 3 (Network layer) test.
Directly measuring the RF environment (the Physical layer 1 test) is the most commonly used approach. Typically, two or more technicians or engineers are involved, one with a transmitting device and the other with one or more receiving devices that report both the characteristics of the transmitted test signal (providing data to determine the propagation channel characteristics) and presence and characteristics of interference signals. Many known tools for wireless performance measurement have been designed to be operated by trained technical personnel, or even by network engineers who are adapting the wireless network design at the same time that the testing tool (possibly itself a wireless network) is being configured to operate correctly.
Direct measurement of the RF environment has several disadvantages: (1) the effort is intensive (trained personnel are required with generally several person-hours of effort per hour of measurement); (2) it is difficult to test many points for many hours; (3) wireless communication performance must be inferred (it is not directly measured); (4) test instruments are relatively expensive (many times more expensive than communication nodes); and (5) test instruments incorporate radios and antennas different from those used in deployed wireless devices.
In some cases, the disadvantages of direct RF measurement can be overcome by installing a network of communicating nodes and measuring the performance during predetermined communication tasks. If it is not difficult to install the communicating nodes, then the manpower requirement may be slight. If there is an adequate mechanism for handling the data, then the network of communicating nodes can operate for an extended period of time and the wireless communication performance is directly measured. Typically, the communicating nodes will not be expensive, particularly if they can be used for many tests. However, operating a communication network to measure communication characteristics has two significant disadvantages. First, the communication network must be operational. Second, the Network layer protocol introduces bias into the measurements.
For a variety of common actions (such as acknowledging the receipt of a packet), networks generate and send control packets, which are separate from and in addition to data carrying packets. Network procedures (protocols) are designed to operate with lost or corrupted packets (e.g., with a variety of timers and counters, if a packet is lost, no acknowledgement comes and the original packet is resent; if an acknowledgement is lost, the packet also is resent; if a packet is corrupted, its re-transmission is requested). If many data-carrying packets are lost, then the network will have low data capacity, which is part of the desired measurement. However, if too many control packets are lost, then the network will cease to function (it is said to collapse). In this case, no measurement is made. A typical rule of thumb is that at least ⅓ of packets must arrive at their destination to avoid network collapse.
Thus, layer 1 tests can operate under very general circumstances, but are effort intensive and limited in other ways. Layer 3 tests operate only where network communications are relatively good.
In many cases it is desirable to coordinate the actions of communicating devices, which requires synchronization among the devices. There are many prior proposals for synchronizing communicating devices, but these are suited for and often require a medium to high rate of successful delivery of messages for correct operation. In a wireless system, a medium to high rate of successful delivery can correspond, for example, to a packet success rate (PSR) of at least 25%.
In some cases, wireless communicating devices operate with a very low PSR. Additionally, some known systems incorporate a master clock and will not function correctly if communicating devices cannot receive messages from the devices with the master clock.
A Kalman filter is an efficient recursive filter which estimates the state of a dynamic system from a series of incomplete and noisy measurements. For example, in a radar application, where one is interested in tracking a target, information about the location, speed and acceleration of the target is measured with a great deal of corruption by noise at any time instant. The Kalman filter exploits the dynamics of the target, which govern its time evolution, to remove the effects of the noise and get a good estimate of the location of the target at the present time (filtering), at a future time (prediction), or at a time in the past (interpolation or smoothing). The Kalman filter is a pure time domain filter, in which only the estimated state from the previous time step and the current measurement are needed to compute the estimate for the current state. In contrast to batch estimation techniques, no history of observations and/or estimates are required. The state of the filter is represented by two variables: (1) the estimate of the state at time k; and (2) the error covariance matrix (a measure of the estimated accuracy of the state estimate). The Kalman filter has two distinct phases: Predict (process update) and Update (measurement update). The Predict phase uses the estimate from the previous time step to produce an estimate of the current state. In the Update phase, measurement information from the current time step is used to refine this prediction to arrive at a new, (hopefully) more accurate estimate.
There is room for improvement in wireless communication systems in which wireless nodes must be synchronized.
There is also room for improvement in methods for synchronizing wireless nodes of a wireless communication network.